MACHINE LEARNING IN EDUCATION: PREDICTING STUDENT SUCCESS USING DATA ANALYTICS
Keywords:
machine learning, education, data analytics, student successAbstract
This study explores the application of machine learning (ML) in education, specifically its potential to predict student success using data analytics. By analyzing historical data such as grades, attendance, and engagement metrics, machine learning algorithms can identify patterns and factors that influence student performance. The research examines various ML models, including decision trees, support vector machines, and neural networks, to predict outcomes such as graduation rates, subject mastery, and overall academic achievement. The study assesses the accuracy and effectiveness of these predictive models in providing early interventions for at-risk students, improving retention, and optimizing instructional strategies. Ethical considerations regarding data privacy, bias, and the transparency of algorithms are also discussed. Findings suggest that machine learning has significant potential to enhance personalized learning, but careful implementation is required to ensure fairness and equity.
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